Predictive Analytics Improve Patient Care: A HealthTech Report


October 1, 2019 by Siobhan Climer

Here’s a quick first aid assessment. Three patients enter the emergency department. Patient A has a fever of 103.2°F and is complaining of a headache. Patient B has a venous wound along their leg and is bleeding heavily. Patient C is complaining of heartburn, dizziness, and some minor chest pain.

predictive analytics improve patient careWho do you care for first?

These types of triage assignments are common for ER nurses and those in the medical profession to perform. These skilled professionals typically use a five-level triage system to assess and admit patients.

While the five-level triage system is more valid and reliable than more rudimentary systems (i.e. a three-level system), hospitals and the healthcare industry as a whole are moving towards relying on predictive analytics that are more accurate, unbiased, and successful. In fact, 57% of executives forecast predictive analytics will save the organization 15% or more over the next five years.


Why Are Emergency Departments Moving Toward Predictive Analytics?


Mistakes in initial patient triage assessment lead to several issues, both for healthcare institutions and patients: excess expenses, stretched resources, and delayed care. recently reported on “Technology-driven Chest Pain Management in the ED”, a predictive analytics project on which Illinois’ NorthShore University HealthSystem recently embarked and earned itself a CIO 100 Award in Excellence.

Think back to Patient C, who is displaying symptoms of a potential heart attack (heartburn, dizziness, and some minor chest pain). “Chest pain is the most common reason that ED staff elect to keep patients for observation in Northshore’s emergency departments,” Thor Olavsrud writes about the project in


How Predictive Analytics Improve Patient Care?


predictive analytics improve patient care


Northshore HealthSystems created a specialized team to help address the fact that over-identifying heart attack risks’ put unnecessary strain on the hospital’s resources. The diverse team of leadership, nurses, ED staff, and finance identified the HEART score (History, Electrocardiogram, Age, Risk factors, and initial Troponin), which integrates risk assessments with patients’ electronic medical records (EMR).

With the EMR acting as a central data platform for each patient, and with a predictive analytics-based alert system, patients receive better care. This methodology has led to a decrease in patients falsely admitted for heart attacks, as well as a decrease in ED returns, mortality, and morbidity.


The Future Of Predictive Analytics In Healthcare


According to an Intel report citing Healthcare Disrupted: Next Generation Business Models and Strategies, “digitization has allowed us to have a more meaningful conversation around how to use predictive analytics to improve patient outcomes.” 93% of health payers and providers believe that predictive analytics is important to the future of their business.

How do healthcare institutions and health clinics ensure they are prepared for the influx of EMR-integrated predictive analytics? Key to successful implementation is a strategic deployment of network infrastructure. Reliable internet access, reliable security, stable data centers, and constant uptime are vital to continued patient care.

Mindsight can help prepare your organization for the technology of tomorrow. Talk with our experts today to learn how you can get started.

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About The Author

Siobhan Climer, Science and Technology Writer for Mindsight, writes about technology trends in education, healthcare, and business. She writes extensively about cybersecurity, disaster recovery, cloud services, backups, data storage, network infrastructure, and the contact center. When she’s not writing tech, she’s reading and writing fantasy, gardening, and exploring the world with her twin daughters. Find her on twitter @techtalksio.

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